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The law of large numbers as well as the central limit theorem are partial solutions to a general problem: "What is the limiting behavior of S n as n approaches infinity?" In mathematical analysis, asymptotic series are one of the most popular tools employed to approach such questions.
The central limit theorem can provide more detailed information about the behavior of than the law of large numbers. For example, we can approximately find a tail probability of M N {\displaystyle M_{N}} – the probability that M N {\displaystyle M_{N}} is greater than some value x {\displaystyle x} – for a fixed value of N {\displaystyle N} .
Donsker's invariance principle for simple random walk on .. In probability theory, Donsker's theorem (also known as Donsker's invariance principle, or the functional central limit theorem), named after Monroe D. Donsker, is a functional extension of the central limit theorem for empirical distribution functions.
Then according to the central limit theorem, the distribution of Z n approaches the normal N(0, 1 / 3 ) distribution. This convergence is shown in the picture: as n grows larger, the shape of the probability density function gets closer and closer to the Gaussian curve.
This theorem can be used to disprove the central limit theorem holds for by using proof by contradiction. This procedure involves proving that Lindeberg's condition fails for X k {\displaystyle X_{k}} .
Central limit theorem. Illustration of the central limit theorem; Concrete illustration of the central limit theorem; Berry–Esséen theorem; Berry–Esséen theorem; De Moivre–Laplace theorem; Lyapunov's central limit theorem; Misconceptions about the normal distribution; Martingale central limit theorem; Infinite divisibility (probability)
Pages in category "Central limit theorem" The following 11 pages are in this category, out of 11 total. This list may not reflect recent changes. ...
The central limit theorem gives only an asymptotic distribution. As an approximation for a finite number of observations, it provides a reasonable approximation only when close to the peak of the normal distribution; it requires a very large number of observations to stretch into the tails.